OpenRaiser/Intern-Atlas huggingface dataset:
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning
Venue: arXiv cs.CV 2018
Year: 2018

Recent approaches on visual scene understanding attempt to build a scene graph -- a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motion. Indeed, in such cases, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.
Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging
Venue: arXiv cs.CV 2018
Year: 2018

Detection of pulmonary nodules in chest CT imaging plays a crucial role in early diagnosis of lung cancer. Manual examination is highly time-consuming and error prone, calling for computer-aided detection, both to improve efficiency and reduce misdiagnosis. Over the years, a range of systems have been proposed, mostly following a two-phase paradigm with: 1) candidate detection, 2) false positive reduction. Recently, deep learning has become a dominant force in algorithm development. As for candidate detection, prior art was mainly based on the two-stage Faster R-CNN framework, which starts with an initial sub-net to generate a set of class-agnostic region proposals, followed by a second sub-net to perform classification and bounding-box regression. In contrast, we abandon the conventional two-phase paradigm and two-stage framework altogether and propose to train a single network for end-to-end nodule detection instead, without transfer learning or further post-processing. Our feature learning model is a modification of the ResNet and feature pyramid network combined, powered by RReLU activation. The major challenge is the condition of extreme inter-class and intra-class sample imbalance, where the positives are overwhelmed by a large negative pool, which is mostly composed of easy and a handful of hard negatives. Direct training on all samples can seriously undermine training efficacy. We propose a patch-based sampling strategy over a set of regularly updating anchors, which narrows sampling scope to all positives and only hard negatives, effectively addressing this issue. As a result, our approach substantially outperforms prior art in terms of both accuracy and speed. Finally, the prevailing FROC evaluation over [1/8, 1/4, 1/2, 1, 2, 4, 8] false positives per scan, is far from ideal in real clinical environments. We suggest FROC over [1, 2, 4] false positives as a better metric.
Professional Development of Teachers Using Cloud Services During Non-formal Education
Venue: arXiv cs.CY 2018
Year: 2018

The rapid development of cloud services and their implementation in secondary education require an increase in the IC-competence of teachers during non-formal education. The implementation of cloud services will make it possible to create some conditions for learning mobility of all participants of teaching and learning activities. The rapid development of cloud services and their implementation in secondary education require an increase in the IC-competence of teachers during non-formal education. The implementation of cloud services will make it possible to create some conditions for learning mobility of all participants of teaching and learning activities. The paper analyzes the main forms of the organization of teachers learning (workshops, trainings and summer schools). The special features of the non-formal teachers ICT training include the availability of high-speed Internet and some computer equipment. The obtained basic and additional services allow teachers to make the extensive use of cloud services for different activities, namely the organization of students group work and inverted learning, team-work on projects, assistance during homework, preparation for contests, conducting web-quests.
The Evolution of Teaching Methods of Students in Electronic Social Networks
Venue: arXiv cs.CY 2018
Year: 2018

The use of electronic social networks (ESN) as a new teaching method that is chosen by modern teachers, who have sufficient professional, information and communication competence, passed several stages of formation and nowadays are widely used in the educational process in secondary schools. The article deals with basic approaches to determining teaching methods in secondary schools through electronic social networks. Main approaches to the selection of teaching methods, particularly through the internal logical path of learning, are defined. Authors demonstrate such teaching methods as: comparison, synthesis, summing up, specification and classification; analytical, synthetic and application of formative assessment. Examples and algorithm of use for each method are given in the article. It is established that the effectiveness of teaching methods depends not only on the same methods but on skills of a teacher to use the functionality of social networks and ICT.
An Efficient Technique for Protecting Location Privacy of Cooperative Spectrum Sensing Users
Venue: arXiv cs.NI 2018
Year: 2018

Cooperative spectrum sensing, despite its effectiveness in enabling dynamic spectrum access, suffers from location privacy threats, merely because secondary users (SUs)' sensing reports that need to be shared with a fusion center to make spectrum availability decisions are highly correlated to the users' locations. It is therefore important that cooperative spectrum sensing schemes be empowered with privacy-preserving capabilities so as to provide SUs with incentives for participating in the sensing task. In this paper, we propose an efficient privacy-preserving protocol that uses an additional architectural entity and makes use of various cryptographic mechanisms to preserve the location privacy of SUs while performing reliable and efficient spectrum sensing. We show that not only is our proposed scheme secure and more efficient than existing alternatives, but also achieves fault tolerance and is robust against sporadic network topological changes.
Towards a Circular Economy via Intelligent Metamaterials
Venue: arXiv cs.OH 2018
Year: 2018

The present study proposes the use of intelligent metasurfaces in the design of products, as enforcers of circular economy principles. Intelligent metasurfaces can tune their physical properties (electromagnetic, acoustic, mechanical) by receiving software commands. When incorporated within products and spaces they can mitigate the resource waste caused by inefficient, partially optimized designs and security concerns. Thus, circular economy and fast-paced product design become compatible. The study begins by considering electromagnetic metamaterials, and proposes a complete methodology for their deployment. Finally, it is shown that the same principles can be extended to the control of mechanical properties of objects, exemplary enabling the micro-management of vibrations and heat, with unprecedented circular economy potential.
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Venue: arXiv cs.CV 2018
Year: 2018

In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datasets. The limited availability of such data is due to the time cost and human effort required to accurately and consistently label real images on a pixel level. Modern sandbox video game engines provide open world environments where traffic and pedestrians behave in a pseudo-realistic manner. This caters well to the collection of a believable road-scene dataset. Utilizing open-source tools and resources found in single-player modding communities, we provide a method for persistent, ground truth, asset annotation of a game world. By collecting a synthetic dataset containing upwards of $1,000,000$ images, we demonstrate real-time, on-demand, ground truth data annotation capability of our method. Supplementing this synthetic data to Cityscapes dataset, we show that our data generation method provides qualitative as well as quantitative improvements---for training networks---over previous methods that use video games as surrogate.
Computer science students simulation in capturing tacit knowledge by using NGT for reducing traffic jam
Venue: arXiv cs.CY 2018
Year: 2018

The subject knowledge management systems is one of the main courses in information systems study program, faculty of computer science. This course is offered at A concentration for students in semester 5 (five). Knowledge consists of knowledge explicit and tacit. To capture tacit knowledge, it can be done by involving a number of methods, namely: 1) Brainstorming, and 2) Nominal Group Technique. The study also involves a number of social information technologies, such as: 1) Facebook, 2) WordPress, 3) DropBox, and 4) YouTube. The topic for the theme of knowledge is how to reduce traffic jam. After passing 2 (two) simulation rounds, this research get 8 (eight) ideas suggestion related to vehicle parking in an effort to reduce traffic jam. The tacit knowledge capture simulation with NGT is able to provide acceptable suggestions by all the panelists involved. The use of social information technology in this study also received a very good response from the panelists.
Don't get Lost in Negation: An Effective Negation Handled Dialogue Acts Prediction Algorithm for Twitter Customer Service Conversations
Venue: arXiv cs.CL 2018
Year: 2018

In the last several years, Twitter is being adopted by the companies as an alternative platform to interact with the customers to address their concerns. With the abundance of such unconventional conversation resources, push for developing effective virtual agents is more than ever. To address this challenge, a better understanding of such customer service conversations is required. Lately, there have been several works proposing a novel taxonomy for fine-grained dialogue acts as well as develop algorithms for automatic detection of these acts. The outcomes of these works are providing stepping stones for the ultimate goal of building efficient and effective virtual agents. But none of these works consider handling the notion of negation into the proposed algorithms. In this work, we developed an SVM-based dialogue acts prediction algorithm for Twitter customer service conversations where negation handling is an integral part of the end-to-end solution. For negation handling, we propose several efficient heuristics as well as adopt recent state-of- art third party machine learning based solutions. Empirically we show model's performance gain while handling negation compared to when we don't. Our experiments show that for the informal text such as tweets, the heuristic-based approach is more effective.
Layer-wise Relevance Propagation for Explainable Recommendations
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we tackle the problem of explanations in a deep-learning based model for recommendations by leveraging the technique of layer-wise relevance propagation. We use a Deep Convolutional Neural Network to extract relevant features from the input images before identifying similarity between the images in feature space. Relationships between the images are identified by the model and layer-wise relevance propagation is used to infer pixel-level details of the images that may have significantly informed the model's choice. We evaluate our method on an Amazon products dataset and demonstrate the efficacy of our approach.
Explanations for Temporal Recommendations
Venue: arXiv cs.AI 2018
Year: 2018

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.
Genetic algorithms in Forth
Venue: arXiv cs.NE 2018
Year: 2018

A method for automatically finding a program (bytecode) realizing the given algorithm is developed. The algorithm is specified as a set of tests (input\_data) $ \rightarrow $ (output\_data). Genetic methods made it possible to find the implementation of relatively complex algorithms: sorting, decimal digits, GCD, LCM, factorial, prime divisors, binomial coefficients, and others. The algorithms are implemented on a highly simplified version of Forth language.
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method for road-objects based on spherical images. We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map. To make PointSeg applicable on a mobile system, we build the model based on the light-weight network, SqueezeNet, with several improvements. It maintains a good balance between memory cost and prediction performance. Our model is trained on spherical images and label masks projected from the KITTI 3D object detection dataset. Experiments show that PointSeg can achieve competitive accuracy with 90fps on a single GPU 1080ti. which makes it quite compatible for autonomous driving applications.
A Survey on Essential Components of a Self-Sovereign Identity
Venue: arXiv cs.CR 2018
Year: 2018

This paper provides an overview of the Self-Sovereign Identity (SSI) concept, focusing on four different components that we identified as essential to the architecture. Self-Sovereign Identity is enabled by the new development of blockchain technology. Through the trustless, decentralised database that is provided by a blockchain, classic Identity Management registration processes can be replaced. We start off by giving a simple overview of blockchain based SSI, introducing an architecture overview as well as relevant actors in such a system. We further distinguish two major approaches, namely the Identifier Registry Model and its extension the Claim Registry Model. Subsequently we discuss identifiers in such a system, presenting past research in the area and current approaches in SSI in the context of Zooko's Triangle. As the user of an SSI has to be linked with his digital identifier we also discuss authentication solutions. Most central to the concept of an SSI are the verifiable claims that are presented to relying parties. Resources in the field are only losely connected. We will provide a more coherent view of verifiable claims in regards to blockchain based SSI and clarify differences in the used terminology. Storage solutions for the verifiable claims, both on- and off-chain, are presented with their advantages and disadvantages.
Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dictionary matching. We evaluate our method on multiparametric brain scans and compare it to three recent MRF reconstruction approaches. Our method achieves state-of-the-art reconstruction accuracy and yields qualitatively more appealing maps compared to other reconstruction methods. In addition, the reconstruction time is significantly reduced compared to a dictionary-based approach.
The altmetric performance of publications authored by Brazilian researchers: analysis of CNPq productivity scholarship holders
Venue: arXiv cs.DL 2018
Year: 2018

The present work seeks to analyse the altmetric performance of Brazilian publications authored by researchers who are productivity scholarship holders (PQ) of the National Council of Scientific and Technological Development (CNPq). It was considered, within the scope of this research, the PQs in activity in October, 2017 (n = 14.609). The scientific production registered on Lattes was collected via GetLattesData and filtered by articles from academic journals published between 2016 and October 2017 that hold the Digital Object Identifier (n = 99064). The online attention data are analysed according to their distribution by density and variation; language of the publication and field of knowledge; and by average performance of the type of source that has provided its altmetric values. The density evidences the long tail behavior of the variable, with most part of the articles with altmetrics score = 0, while few articles have a high index. The average of the online attention indicates a better performance of articles written in English and belonging to the Health and Biological Sciences field of knowledge. As for the sources, there was a good performance from Mendeley, followed by Twitter and a low coverage from Facebook
Tiered Object Storage using Persistent Memory
Venue: arXiv cs.DC 2018
Year: 2018

Most data intensive applications often access only a few fields of the objects they are operating on. Since NVM provides fast, byte-addressable access to durable memory, it is possible to access various fields of an object stored in NVM directly without incurring any serialization and deserialization cost. This paper proposes a novel tiered object storage model that modifies a data structure such that only a chosen subset of fields of the data structure are stored in NVM, while the remaining fields are stored in a cheaper (and a traditional) storage layer such as HDDs/SSDs. We introduce a novel linear-programming based optimization framework for deciding the field placement. Our proof of concept demonstrates that a tiered object storage model improves the execution time of standard operations by up to 50\% by avoiding the cost of serialization/deserialization and by reducing the memory footprint of operations.
Confederated Modular Differential Equation APIs for Accelerated Algorithm Development and Benchmarking
Venue: arXiv cs.SE 2018
Year: 2018

Performant numerical solving of differential equations is required for large-scale scientific modeling. In this manuscript we focus on two questions: (1) how can researchers empirically verify theoretical advances and consistently compare methods in production software settings and (2) how can users (scientific domain experts) keep up with the state-of-the-art methods to select those which are most appropriate? Here we describe how the confederated modular API of DifferentialEquations.jl addresses these concerns. We detail the package-free API which allows numerical methods researchers to readily utilize and benchmark any compatible method directly in full-scale scientific applications. In addition, we describe how the complexity of the method choices is abstracted via a polyalgorithm. We show how scientific tooling built on top of DifferentialEquations.jl, such as packages for dynamical systems quantification and quantum optics simulation, both benefit from this structure and provide themselves as convenient benchmarking tools.
Motion Invariance in Visual Environments
Venue: arXiv cs.CV 2018
Year: 2018

The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as happens in nature. In this paper, we claim that their processing naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of visual learning based on convolutional features. The theory addresses a number of intriguing questions that arise in natural vision, and offers a well-posed computational scheme for the discovery of convolutional filters over the retina. They are driven by the Euler-Lagrange differential equations derived from the principle of least cognitive action, that parallels laws of mechanics. Unlike traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario in which feature learning takes place by unsupervised processing of video signals. An experimental report of the theory is presented where we show that features extracted under motion invariance yield an improvement that can be assessed by measuring information-based indexes.
SPOC: Secure Payments for Outsourced Computations
Venue: arXiv cs.CR 2018
Year: 2018

Constrained devices in IoT networks often require to outsource resource-heavy computations or data processing tasks. Currently, most of those jobs are done in the centralised cloud. However, with rapidly increasing number of devices and amount of produced data, edge computing represents a much more efficient solution decreasing the cost, the delay and improves users' privacy. To enable wide deployment of execution nodes at the edge, the requesting devices require a way to pay for submitted tasks. We present SPOC - a secure payment system for networks where nodes distrust each other. SPOC allows any node to execute tasks, includes result verification and enforce users' proper behaviour without 3rd parties, replication or costly proof of computations. We implement our system using Ethereum Smart Contracts and Intel SGX and present first evaluation proving its security and low usage cost.
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
Venue: arXiv cs.LG 2018
Year: 2018

Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones. We propose a generative adversarial network (GAN) approach for predicting desirable 3D dose distributions that eschews the previous paradigms of site-specific feature engineering and predicting low-dimensional representations of the plan. Experiments on a dataset of oropharyngeal cancer patients show that our approach significantly outperforms previous methods on several clinical satisfaction criteria and similarity metrics.